Risk factors for mortality in surgical patients on combined continuous renal replacement therapy and extracorporeal membrane oxygenation: single-center retrospective study.

OBJECTIVE: In patients receiving extracorporeal membrane oxygenation (ECMO), continuous renal replacement therapy (CRRT) is increasingly being used for renal replacement and fluid management. However, critically ill surgical patients receiving combined ECMO and CRRT tend to have a high mortality rate, and there are limited studies on this population. Therefore, we aimed to investigate the risk factors for mortality in surgical patients receiving combined ECMO and CRRT. METHODS: Data of surgical patients who underwent ECMO between December 2013 and April 2023 were retrospectively reviewed. Univariate and multivariate logistic regression analysis were used to identify the risk variables. Receiver operating characteristic (ROC) curve analysis was used to determine the cutoff value of albumin and age to predict death. RESULTS: A total of 199 patients on ECMO support were screened, of which 105 patients were included in the final analysis. Of 105 patients, 77 (73.33%) were treated with CRRT. Veno-arterial ECMO was performed in 97 cases (92.38%), and the rest were veno-venous ECMO (n = 8, 7.62%). Cardiovascular-related surgery was performed in the main patients (n = 86, 81.90%) and other types of surgery in 19 patients. In surgical patients on ECMO support, the logistic regression analysis showed that CRRT implantation, male sex, and age were the independent risks factors for mortality. Furthermore, the ROC curve analysis showed that age 48.5 years had the highest Youden index. In surgical patients on combined CRRT and ECMO, age, valvular heart disease, and albumin were the independent risk factors for prognosis. Albumin had the highest Youden index at a cutoff value of 39.95 g/L for predicting mortality, though the overall predictive value was modest (area under ROC 0.704). Age had the highest Youden index at a cutoff value of 48.5 years for predicting mortality. CONCLUSIONS: In our cohort of surgical patients requiring ECMO, which consisted mostly of patients undergoing cardiovascular surgery requiring VA-ECMO, the need for CRRT was an independent risk factor for mortality. In the subset of patients on combined CRRT and ECMO, independent risk factors for mortality included higher age, lack of valvular heart disease, and lower serum albumin.

Characterization of a novel sucrose phosphorylase from Paenibacillus elgii and its use in biosynthesis of α-arbutin.

α-Arbutin, a naturally occurring glycosylated derivative of hydroquinone (HQ), effectively inhibits melanin biosynthesis in epidermal cells. It is widely recognized as a fourth-generation whitening agent within the cosmetic industry. Currently, enzymatic catalysis is universally deemed the safest and most efficient method for α-arbutin synthesis. Sucrose phosphorylase (SPase), one of the most frequently employed glycosyltransferases, has been extensively reported for α-arbutin synthesis. In this study, a previously reported SPase known for its effectiveness in synthesizing α-arbutin, was used as a probe sequence to identify a novel SPase from Paenibacillus elgii (PeSP) in the protein database. The sequence similarity between PeSP and the probe was 39.71%, indicating a degree of novelty. Subsequently, the gene encoding PeSP was coexpressed with the molecular chaperone pG-Tf2 in Escherichia coli, significantly improving PeSP's solubility. Following this, PeSP was characterized and employed for α-arbutin biosynthesis. The specific activity of co-expressed PeSP reached 169.72 U/mg, exhibited optimal activity at 35℃ and pH 7.0, with a half-life of 3.6 h under the condition of 35℃. PeSP demonstrated excellent stability at pH 6.5-8.5 and sensitivity to high concentrations of metal ions. The kinetic parameters Km and kcat/Km were determined to be 14.50 mM and 9.79 min- 1·mM- 1, respectively.The reaction conditions for α-arbutin biosynthesis using recombinant PeSP were optimized, resulting in a maximum α-arbutin concentration of 52.60 g/L and a HQ conversion rate of 60.9%. The optimal conditions were achieved at 30℃ and pH 7.0 with 200 U/mL of PeSP, and by combining sucrose and hydroquinone at a molar ratio of 5:1 for a duration of 25 h.

Recent progress in fish oil-based emulsions by various food-grade stabilizers: Fabrication strategy, interfacial stability mechanism and potential application.

Fish oil, rich in a variety of long-chain ω-3 PUFAs, is widely used in fortified foods due to its broad-spectrum health benefits. However, its undesired characteristics include oxidation sensitivity, poor water solubility, and fishy off-flavor greatly hinder its exploitation in food field. Over the past two decades, constructing fish oil emulsions to encapsulate ω-3 PUFAs for improving their physicochemical and functional properties has undergone great progress. This review mainly focuses on understanding the fabrication strategies, stabilization mechanism, and potential applications of fish oil emulsions, including fish oil microemulsions, nanoemulsions, double emulsions, Pickering emulsions and emulsion gels. Furthermore, the role of oil-water interfacial stabilizers in the fish oil emulsions stability will be discussed with a highlight on food-grade single emulsifiers and natural complex systems for achieving this purpose. Additionally, its roles and applications in food industry and nutrition field are delineated. Finally, possible innovative food trends and applications are highlighted, such as novel fish oil-based delivery systems construction (e.g., Janus emulsions and nutraceutical co-delivery systems), exploring digestion and absorption mechanisms and enhancing functional evaluation (e.g., nutritional supplement enhancer, and novel fortified/functional foods). This review provides a reference for the application of fish oil-based emulsion systems in future precision diet intervention implementations.

Fault Detection for Wind Turbine Blade Bolts Based on GSG Combined with CS-LightGBM.

Aiming at the problem of class imbalance in the wind turbine blade bolts operation-monitoring dataset, a fault detection method for wind turbine blade bolts based on Gaussian Mixture Model-Synthetic Minority Oversampling Technique-Gaussian Mixture Model (GSG) combined with Cost-Sensitive LightGBM (CS-LightGBM) was proposed. Since it is difficult to obtain the fault samples of blade bolts, the GSG oversampling method was constructed to increase the fault samples in the blade bolt dataset. The method obtains the optimal number of clusters through the BIC criterion, and uses the GMM based on the optimal number of clusters to optimally cluster the fault samples in the blade bolt dataset. According to the density distribution of fault samples in inter-clusters, we synthesized new fault samples using SMOTE in an intra-cluster. This retains the distribution characteristics of the original fault class samples. Then, we used the GMM with the same initial cluster center to cluster the fault class samples that were added to new samples, and removed the synthetic fault class samples that were not clustered into the corresponding clusters. Finally, the synthetic data training set was used to train the CS-LightGBM fault detection model. Additionally, the hyperparameters of CS-LightGBM were optimized by the Bayesian optimization algorithm to obtain the optimal CS-LightGBM fault detection model. The experimental results show that compared with six models including SMOTE-LightGBM, CS-LightGBM, K-means-SMOTE-LightGBM, etc., the proposed fault detection model is superior to the other comparison methods in the false alarm rate, missing alarm rate and F1-score index. The method can well realize the fault detection of large wind turbine blade bolts.

Fault Detection of Wind Turbine Gearboxes Based on IBOA-ERF.

As one of the key components of wind turbines, gearboxes are under complex alternating loads for a long time, and the safety and reliability of the whole machine are often affected by the failure of internal gears and bearings. Aiming at the difficulty of optimizing the parameters of wind turbine gearbox fault detection models based on extreme random forest, a fault detection model with extreme random forest optimized by the improved butterfly optimization algorithm (IBOA-ERF) is proposed. The algebraic sum of the false alarm rate and the missing alarm rate of the fault detection model is constructed as the fitness function, and the initial position and position update strategy of the individual are improved. A chaotic mapping strategy is introduced to replace the original population initialization method to enhance the randomness of the initial population distribution. An adaptive inertia weight factor is proposed, combined with the landmark operator of the pigeon swarm optimization algorithm to update the population position iteration equation to speed up the convergence speed and improve the diversity and robustness of the butterfly optimization algorithm. The dynamic switching method of local and global search stages is adopted to achieve dynamic balance between global exploration and local search, and to avoid falling into local optima. The ERF fault detection model is trained, and the improved butterfly optimization algorithm is used to obtain optimal parameters to achieve fast response of the proposed model with good robustness and generalization under high-dimensional data. The experimental results show that, compared with other optimization algorithms, the proposed fault detection method of wind turbine gearboxes has a lower false alarm rate and missing alarm rate.

Determining the invasiveness of ground-glass nodules using a 3D multi-task network.

OBJECTIVES: The aim of this study was to determine the invasiveness of ground-glass nodules (GGNs) using a 3D multi-task deep learning network. METHODS: We propose a novel architecture based on 3D multi-task learning to determine the invasiveness of GGNs. In total, 770 patients with 909 GGNs who underwent lung CT scans were enrolled. The patients were divided into the training (n = 626) and test sets (n = 144). In the test set, invasiveness was classified using deep learning into three categories: atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive pulmonary adenocarcinoma (IA). Furthermore, binary classifications (AAH/AIS/MIA vs. IA) were made by two thoracic radiologists and compared with the deep learning results. RESULTS: In the three-category classification task, the sensitivity, specificity, and accuracy were 65.41%, 82.21%, and 64.9%, respectively. In the binary classification task, the sensitivity, specificity, accuracy, and area under the ROC curve (AUC) values were 69.57%, 95.24%, 87.42%, and 0.89, respectively. In the visual assessment of GGN invasiveness of binary classification by the two thoracic radiologists, the sensitivity, specificity, and accuracy of the senior and junior radiologists were 58.93%, 90.51%, and 81.35% and 76.79%, 55.47%, and 61.66%, respectively. CONCLUSIONS: The proposed multi-task deep learning model achieved good classification results in determining the invasiveness of GGNs. This model may help to select patients with invasive lesions who need surgery and the proper surgical methods. KEY POINTS: • The proposed multi-task model has achieved good classification results for the invasiveness of GGNs. • The proposed network includes a classification and segmentation branch to learn global and regional features, respectively. • The multi-task model could assist doctors in selecting patients with invasive lesions who need surgery and choosing appropriate surgical methods.

Development and validation of a multivariable predictive model for EGFR gene mutation status in patients with lung adenocarcinoma.

Detection of epidermal growth factor receptor (EGFR) is one real dilemma owing to the non-sufficient tissue for testing EGFR mutations in lung adenocarcinoma. A model for predicting EGFR mutations would be helpful for clinical decisions in those patients. A retrospective cohort of 1,196 patients diagnosed with lung adenocarcinoma was investigated between December 1, 2017, and December 31, 2019, in Renji Hospital, Shanghai, China. All patients were tested for EGFR mutations (amplification refractory mutation system, n=1,144; next-generation sequencing, n=52). Of 1,196 patients with lung adenocarcinoma, 944 met the inclusion criteria. A nomogram model was developed based on 567 patients and validated in 377 patients. Variables associated with EGFR mutations were age, sex, smoking history, lepidic predominant subtype, solid predominant subtype, mucinous adenocarcinoma, Ki67 expression, lobulation, solid texture in radiology, and pleural retraction. The nomogram based on the model performed well in the development group (c-index 0.789, 95% CI: 0.751-0.827), and the validation group (c-index 0.809, 95% CI: 0.771-0.847). At the probability cut-point of 0.7, the diagnostic efficiency was 82.7% in patients with NGS liquid biopsy. Decision curve analysis further confirmed the clinical usefulness of the nomogram, which showed that predicting the EGFR mutations probability applying this nomogram would be better than having all patients or none patients use this nomogram. A high probability group (>0.7) by nomogram model may suggest a high possibility of EGFR mutation, if tissue is limited, NGS-based ctDNA with liquid biopsy could be implemented effectively.

Fault Detection of Wind Turbine Electric Pitch System Based on IGWO-ERF.

It is difficult to optimize the fault model parameters when Extreme Random Forest is used to detect the electric pitch system fault model of the double-fed wind turbine generator set. Therefore, Extreme Random Forest which was optimized by improved grey wolf algorithm (IGWO-ERF) was proposed to solve the problems mentioned above. First, IGWO-ERF imports the Cosine model to nonlinearize the linearly changing convergence factor α to balance the global exploration and local exploitation capabilities of the algorithm. Then, in the later stage of the algorithm iteration, α wolf generates its mirror wolf based on the lens imaging learning strategy to increase the diversity of the population and prevent local optimum of the population. The electric pitch system fault detection method of the wind turbine generator set sets the generator power of the variable pitch system as the main state parameter. First, it uses the Pearson correlation coefficient method to eliminate the features with low correlation with the electric pitch system generator power. Then, the remaining features are ranked by the importance of the RF features. Finally, the top N features are selected to construct the electric pitch system fault data set. The data set is divided into a training set and a test set. The training set is used to train the proposed fault detection model, and the test set is used for testing. Compared with other parameter optimization algorithms, the proposed method has lower FNR and FPR in the electric pitch system fault detection of the wind turbine generator set.

Strong adsorption of phosphate by amorphous lanthanum carbonate nano-adsorbents.

Phosphorus removal is a crucial aspect of controlling water pollution and eutrophication. In this study, the preparation of lanthanum carbonate (LC) nano-adsorbents for the efficient removal of phosphate (P) from water and wastewater was investigated. Results from XRD, SEM and Zeta potential analyses revealed that addition of magnesium ions and adjustment of the reaction temperature could control the morphology and microstructure of LC. Effects of initial pH, adsorbent dosage, contact time, and the water matrix on P adsorption were investigated. Batch adsorption experiments revealed that LC showed strong performance on P removal over a wide pH range (3.0 to 11.0). The kinetic data followed a pseudo-second-order model, and equilibrium data were well fitted by the Langmuir model with a maximum adsorption capacity of 112.9 mg P/g. Adsorption thermodynamics showed that the adsorption process was exothermic and spontaneous. Results of a monolayer model for single adsorption indicated that P could completely interact with two or more functional groups from the LC surface. In the presence of competing ions (F-, Cl-, SO42-, NO3-, and HCO3-), LC maintained high selectivity for phosphate. For a real effluent, the P concentration was efficiently reduced from 3.2 mg P/L to below 0.5 mg P/L at a dose of 0.5 g/L LC. All the results suggested that LC can serve as a promising adsorbent for P removal in a wide range of pH, and thus could meet the stricter discharge regulations from actual wastewater.

Design and Analysis of the IGBT Heat Dissipation Structure Based on Computational Continuum Mechanics.

With the trend of high integration and high power of insulated gate bipolar transistor (IGBT) components, strict requirements have been placed on the heat dissipation capabilities of the IGBT devices. On the basis of traditional rectangular fins, this paper developed two new types of heat-dissipating fins to meet the high requirements of heat dissipation for the IGBT devices. One is the rectangular radiator with a groove length of 2.5 mm and a width of 0.85 mm, the other is the arc radiator with the angle of 125 arc angle, 0.8 mm arc height, and 1.4 mm circle radius. After theoretically calculating the IGBT junction temperature, numerical simulations have been implemented to verify the theoretical result. The commercial CFD software, STAR-CCM+, was employed to simulate the heat dissipation characteristics of the IGBT module under different wind speeds, power, and fin structures. By analyzing the temperature field and vector field of the IGBT module, the analysis results demonstrate that the error between the simulation result and the theoretical calculation is within 5%, which proves the feasibility of the newly designed heat-dissipating fins. When the wind speed is 12.5 m/s, the power is 110 W, the fin height is 31.2 mm, and the fin thickness is 2.3 mm, the rectangular radiator can achieve the best heat dissipation performance.

A New Switched State Jump Observer for Traffic Density Estimation in Expressways Based on Hybrid-Dynamic-Traffic-Network-Model.

When faced with problems such as traffic state estimation, state prediction, and congestion identification for the expressway network, a novel switched observer design strategy with jump states is required to reconstruct the traffic scene more realistically. In this study, the expressway network is firstly modeled as the special discrete switched system, which is called the piecewise affine system model, a partition of state subspace is introduced, and the convex polytopes are utilized to describe the combination modes of cells. Secondly, based on the hybrid dynamic traffic network model, the corresponding switched observer (including state jumps) is designed. Furthermore, by applying multiple Lyapunov functions and S-procedure theory, the observer design problem can be converted into the existence issue of the solutions to the linear matrix inequality. As a result, a set of gain matrices can be obtained. The estimated states start to jump when the mode changes occur, and the updated value of the estimated state mainly depends on the estimated and the measured values at the previous time. Lastly, the designed state jump observer is applied to the Beijing Jingkai expressway, and the superiority and the feasibility are demonstrated in the application results.

Preliminary exploration of survival analysis using the OHDSI common data model: a case study of intrahepatic cholangiocarcinoma.

BACKGROUND: Data heterogeneity is a common phenomenon related to the secondary use of electronic health records (EHR) data from different sources. The Observational Health Data Sciences and Informatics (OHDSI) Common Data Model (CDM) organizes healthcare data into standard data structures using concepts that are explicitly and formally specified through standard vocabularies, thereby facilitating large-scale analysis. The objective of this study is to design, develop, and evaluate generic survival analysis routines built using the OHDSI CDM. METHODS: We used intrahepatic cholangiocarcinoma (ICC) patient data to implement CDM-based survival analysis methods. Our methods comprise the following modules: 1) Mapping local terms to standard OHDSI concepts. The analytical expression of variables and values related to demographic characteristics, medical history, smoking status, laboratory results, and tumor feature data. These data were mapped to standard OHDSI concepts through a manual analysis; 2) Loading patient data into the CDM using the concept mappings; 3) Developing an R interface that supports the portable survival analysis on top of OHDSI CDM, and comparing the CDM-based analysis results with those using traditional statistical analysis methods. RESULTS: Our dataset contained 346 patients diagnosed with ICC. The collected clinical data contains 115 variables, of which 75 variables were mapped to the OHDSI concepts. These concepts mainly belong to four domains: condition, observation, measurement, and procedure. The corresponding standard concepts are scattered in six vocabularies: ICD10CM, ICD10PCS, SNOMED, LOINC, NDFRT, and READ. We loaded a total of 25,950 patient data records into the OHDSI CDM database. However, 40 variables failed to map to the OHDSI CDM as they mostly belong to imaging data and pathological data. CONCLUSIONS: Our study demonstrates that conducting survival analysis using the OHDSI CDM is feasible and can produce reusable analysis routines. However, challenges to be overcome include 1) semantic loss caused by inaccurate mapping and value normalization; 2) incomplete OHDSI vocabularies describing imaging data, pathological data, and modular data representation.

[Development of Cerebral Oxygen Monitor Based on Near-infrared Spectroscopy].

A system was designed in this paper, which can be used in clinical cerebral oxygen data acquisition and display, it is based on STM32 and NIRS(Near-infrared Spectroscopy). First of all, this paper introduced the basic principle of SaO2 detection, and system scheme. Next, it illustrated the hardware system which is composed of the modules of LED, light source driver, photoelectric detection, and the signal processing. Then, the software system based on uC/OS-Ⅲ of the system was presented. At last, the feasibility and effectiveness of the system were verified by forearm occlusion and 3-D TV experiment. This system is noninvasive, small-volume, mobile-convenient, easy to use and can storage data.

Improving Image Quality for Lung Cancer Imaging With Optimal Monochromatic Energy Level in Dual Energy Spectral Computed Tomography.

OBJECTIVE: The aim of this study was to find optimal monochromatic spectral computed tomography (CT) level to improve image quality of lung cancer. METHODS: Fifty patients with lung cancers were scanned by spectral CT; monochromatic images at 50, 60, 70 and 80 keV energy levels were generated; and objective analysis including image noise, lesion-to-lung contrast-to-noise ratio, and CT number difference between central and peripheral regions of tumor (dCT value) were measured and compared. Subjective assessment about the overall image quality and inhomogeneity enhancement was compared. RESULTS: The highest contrast-to-noise ratio value and subjective score of image quality were obtained at 70 keV, which were superior to those of 50- and 80-keV series (all P < 0.05). The subjective score of the inhomogeneity evaluation was peaked at 60-keV series and significantly higher than other energy levels (all P < 0.05). CONCLUSIONS: Both objective and subjective image analysis of lung cancers may be improved with the combined observation of 60 keV and 70 keV monochromatic images in spectral CT.

Cloning, overexpression and characterization of a thermostable pullulanase from Thermus thermophilus HB27.

A gene encoding a special type of pullulanase from Thermus thermophilus HB27 (TTHpu) was cloned. It has an open reading frame of 1428bp encoding a mature protein with a molecular mass of 52kDa. The gene was expressed in Escherichia coli using pHsh and pET28a vectors. The pHsh expression system produced a 3.6-fold higher recombinant pullulanase than pET28a. The recombinant TTHpu was purified to homogeneity by heat treatment and Ni-NTA affinity chromatography. The purified TTHpu exhibited highest activity at pH 6.5 and 70°C. More than 90% activity was retained after incubation at 60-70°C for 2h and the half-life was 2h at 80°C. The stability of the enzyme was in a pH range from 6.0 to 8.0. Manganese at 5mM enhanced its activity up to 298%. The Km and Vmax for the enzyme activity on pullulan were 0.0031mgmL(-1) and 23.8µmolmin(-1), respectively. Unlike the most of pullulan-hydrolyzing enzymes described to date, this enzyme can attack α-1,6- and α-1,4-glycosidic linkages in pullulan, and produce a mixture of maltotriose, maltose and glucose. The enzyme could be further employed for industrial saccharification of starch.

The RNA-binding domain of DCL3 is required for long-distance RNAi signaling.

Small RNA (sRNA)-mediated RNA silencing (also known as RNA interference, or RNAi) is a conserved mechanism in eukaryotes that includes RNA degradation, DNA methylation, heterochromatin formation and protein translation repression. In plants, sRNAs can move either cell-to-cell or systemically, thereby acting as mobile silencing signals to trigger noncell autonomous silencing. However, whether and what proteins are also involved in noncell autonomous silencing have not been elucidated. In this study, we utilized a previously reported inducible RNAi plant, PDSi, which can induce systemic silencing of the endogenous PDS gene, and we demonstrated that DCL3 is involved in systemic PDS silencing through its RNA binding activity. We confirmed that the C-terminus of DCL3, including the predicted RNA-binding domain, is capable of binding short RNAs. Mutations affecting RNA binding, but not processing activity, reduced systemic PDS silencing, indicating that DCL3 binding to RNAs is required for the induction of systemic silencing. Cucumber mosaic virus infection assays showed that the RNA-binding activity of DCL3 is required for antiviral RNAi in systemically noninoculated leaves. Our findings demonstrate that DCL3 acts as a signaling agent involved in noncell autonomous silencing and an antiviral effect in addition to its previously known function in the generation of 24-nucleotide sRNAs. Supplementary Information: The online version contains supplementary material available at 10.1007/s42994-023-00124-6.

Erratum: Publisher Correction: The RNA-binding domain of DCL3 is required for long-distance RNAi signaling.

[This corrects the article DOI: 10.1007/s42994-023-00124-6.].

Impact of heart failure and preoperative platelet count on the postoperative short-term outcome in infective endocarditis patients.

BACKGROUND: Heart failure (HF) and platelet count are often considered risk factors for mortality in patients with infective endocarditis (IE); however, their effects on various complications have not been elucidated. HYPOTHESIS: We speculated that HF and platelet count have significant impact on the short-term outcomes of IE. METHODS: This single-center retrospective study analyzed data from 320 IE patients who underwent surgery. A multivariate Cox proportional hazards model was used to identify the risk factors for adverse outcomes. The effect of the platelet count on the prognosis of patients with HF was determined by subgroup analysis and Kaplan-Meier analysis. RESULTS: The study population was divided into the HF group (n = 102) and the non-HF group (n = 218). The median age of the total population was 44.5 years (31-56 years), of which 227 (70.94%) patients were male. The incidence rates of 1-year all-cause mortality, cardiac outcomes, and composite outcomes were respectively almost sixfold, fourfold, and threefold higher in the HF group than in the non-HF group (all p < 0.001). In multivariate Cox regression analysis, HF was an independent risk factor for 1-year all-cause mortality, cardiac outcomes, cerebral outcomes, and composite outcomes. The Kaplan-Meier survival curves revealed that the patients with both HF and thrombocytopenia demonstrated the worst composite outcomes than the patients of the other groups (log-rank p < 0.001). In the HF group, the platelet count was significantly associated with mortality and composite outcomes. CONCLUSIONS: HF and preoperative platelet count are significantly associated with 1-year all-cause mortality and adverse outcomes postoperatively in IE patients. Patients with HF and thrombocytopenia have the worst short-term prognosis.

Development and validation of a postoperative bleeding complications prediction model in infective endocarditis.

OBJECTIVES: Bleeding complications are one of the most serious postoperative complications after cardiac surgery and are associated with high mortality, especially in patients with infective endocarditis (IE). Our objectives were to identify the risk factors and develop a prediction model for postoperative bleeding complications in IE patients. METHODS: The clinical data of IE patients treated from October 2013 to January 2022 were reviewed. Multivariate logistic regression analysis was used to evaluate independent risk factors for postoperative bleeding complications and develop a prediction model accordingly. The prediction model was verified in a temporal validation cohort. The performance of the model was evaluated in terms of its discrimination power, calibration, precision, and clinical utility. RESULTS: A total of 423 consecutive patients with IE who underwent surgery were included in the final analysis, including 315 and 108 patients in the training cohort and validation cohort, respectively. Four variables were selected for developing a prediction model, including platelet counts, systolic blood pressure, heart failure and vegetations on the mitral and aortic valves. In the training cohort, the model exhibited excellent discrimination power (AUC = 0.883), calibration (Hosmer-Lemeshow test, P = 0.803), and precision (Brier score = 0.037). In addition, the model also demonstrated good discrimination power (AUC = 0.805), calibration (Hosmer-Lemeshow test, P = 0.413), and precision (Brier score = 0.067) in the validation cohort. CONCLUSIONS: We developed and validated a promising risk model with good discrimination power, calibration, and precision for predicting postoperative bleeding complications in IE patients.

Heat transfer and pressure drop characteristic research of sine wavy flying-wing fins.

In recent years, heat transfer enhancement of heat exchange equipment has attracted more and more attention. In this paper, the heat transfer and pressure drop characteristics of sine wavy flying-wing fins are studied by numerical method. The objective is to improve the integrated heat transfer and pressure drop performance of sine wavy flying-wing fins. The degrees of freedom of fin sizes include fin pitch to fin height ratio fp/fh, fin height to fin wavelength ratio fh/W, fin amplitude to fin pitch ratio 2A/fp and fin inclined angle α. The results show that among the calculated 17 flying-wing fins, the optimal values of fp/fh, fh/W, 2A/fp, and α are 0.5, 0.4, 1.9 and 70° respectively. The optimized SWFWF simulation model is established, and the average JF factor is 1.307, which is about 10.9% higher than that of Fin 05 (JF = 1.18). Multiple linear regression is used to obtain the correlations of flow and heat transfer characteristics of flying-wing fins. The average deviation of the correlations for j and f are 0.85% and 4.9% respectively. The correlations can be used for the design and optimization of sine wavy flying-wing fins.